How Eidos-Montréal created Grid Sensors to improve observations for training agents

Within Eidos Labs, several projects use machine learning. The Automated Game Testing project tackles the problem of testing the functionality of expansive AAA games by modeling player behavior with agents that have learned behavior using reinforcement learning (RL).  In this blog post, we’ll describe how the team at Eidos Labs…Read More

How Metric Validation can help you finetune your game

Over the past year, Unity Game Simulation has enabled developers to balance their games during development by running multiple playthroughs in parallel in the cloud. Today we are excited to share the next step in automating aspects of the balancing workflow by releasing Metric Validation, a precursor to our upcoming…Read More

Training a performant object detection ML model on synthetic data using Unity Perception tools

Supervised machine learning (ML) has revolutionized artificial intelligence and has led to the creation of numerous innovative products. However, with supervised machine learning, there is always a need for larger and more complex datasets, and collecting these datasets is costly. How can you be sure of the label quality? How…Read More

Use Unity’s Understanding tools to generate and Examine synthetic data at scale to train your ML models

Synthetic data alleviates the challenge of acquiring labeled data needed to train machine learning models. In this post, the second in our blog series on synthetic data, we will introduce tools from Unity to generate and analyze synthetic datasets with an illustrative example of object detection. In our first blog…Read More